KGP Talkie
Learning path

Data Science Learning Path

A structured roadmap for mastering data science: Python fundamentals through machine learning, deep learning, NLP, and deployment with FastAPI and Docker.

Start at Stage 01 See full curriculum
5
Structured courses
200,000+
Students enrolled
Self-paced
On demand
The route01Foundations02Stats & Regression03Deep Learning04NLP Mastery05Production ML
Outcomes

Skills You'll Master

01

Python Foundations

Master NumPy, Pandas, Matplotlib, and data visualization workflows.

02

Supervised & Unsupervised ML

Implement regression, decision trees, random forests, KMeans, and PCA.

03

Deep Learning & NLP

Build neural network classifiers and advanced language processing systems.

04

Production Deployment

Package and deploy models as web APIs using FastAPI, Docker, and AWS.

Why the sequence matters

Roadmap Overview

Building a career in data science takes more than isolated tutorials. It takes a path where each stage builds on the last, because reaching for advanced topics before the fundamentals are solid is the most common reason learners plateau.

Here is why the courses are sequenced this way, and what each stage gives us.

Data science is a layered discipline. Regression needs Python and NumPy. Deep learning needs supervised learning. NLP needs deep learning and text preprocessing. Deployment needs all of it, plus software engineering.

Learning these out of order leaves us able to follow a tutorial but unable to adapt it to a new problem. The sequence below closes that gap.

The learning path is divided into five progressive stages:

  1. Foundations -- Python programming, data manipulation, and core ML algorithms (supervised and unsupervised).
  2. Statistical Depth -- Advanced regression techniques, feature engineering, and model interpretability.
  3. Deep Learning -- Neural network architectures (ANN, CNN, RNN, LSTM) using TensorFlow 2.x.
  4. Language Understanding -- Text processing, sentiment analysis, and NLP pipelines with NLTK, SpaCy, and word embeddings.
  5. Production Engineering -- REST API development, Docker containerization, and cloud deployment on AWS.

Each stage is designed to take approximately 2-4 weeks of focused study, with the full path completable in 3-5 months depending on prior experience.

The progression
01Foundations: Machine Learning and Data ScienceFoundation
02Statistical Depth: Advanced RegressionBeginner
03Deep Learning FundamentalsIntermediate
04Natural Language ProcessingAdvanced
05Production DeploymentExpert
5 stages

Pathway Curriculum

Each stage pairs the concepts you need with the course that teaches them.

01
Stage 01

Foundations: Machine Learning and Data Science

This course builds the toolkit every later stage depends on. We start with Python fundamentals, move into NumPy, Pandas, and Matplotlib, then work through supervised learning and unsupervised learning on real datasets.

Topics you will master
  • Python for data manipulation and visualization
  • Supervised learning: Linear Regression, Logistic Regression, KNN, Decision Trees, Random Forest
  • Unsupervised learning: KMeans Clustering, Principal Component Analysis (PCA)
  • Ensemble methods: XGBoost
  • Model evaluation metrics: accuracy, precision, recall, F1, ROC-AUC
  • End-to-end project workflows with real-world datasets
Best forAbsolute beginners, students, and career switchers. No prior machine learning experience is required -- only basic programming familiarity.
Expected outcomeStrong ML foundation and the confidence to tackle intermediate-level problems.
02
Stage 02

Statistical Depth: Advanced Regression

This course goes well beyond simple `LinearRegression().fit()` calls. We cover the intuition behind Lasso and Ridge regularization, build systematic feature selection pipelines, and add the explainability tooling that industry roles now expect.

Topics you will master
  • Linear and non-linear regression techniques
  • Regularization: Lasso (L1) and Ridge (L2) regression
  • Feature selection and transformation pipelines
  • Outlier detection and removal strategies
  • Model explainability using SHAP and LIME
  • Data visualization and interpretation best practices
Best forLearners who finished the foundations stage and want interpretable regression models, especially for finance, healthcare, and other domains where transparency is required.
Expected outcomeBuild and interpret production-ready regression models with full explainability pipelines.
03
Stage 03

Deep Learning Fundamentals

This stage moves from classical algorithms to neural networks with TensorFlow. We start with perceptrons, activation functions, and backpropagation, then work through CNNs for images, RNNs and LSTMs for sequences, and transfer learning with pretrained models.

Topics you will master
  • Neural network architecture from scratch: perceptrons, activation functions, backpropagation
  • Artificial Neural Networks (ANN) for tabular data
  • Convolutional Neural Networks (CNN) for image classification and object recognition
  • Recurrent Neural Networks (RNN) and LSTM for sequence modeling
  • TensorFlow 2.x implementation patterns
  • Transfer learning with pretrained models
  • Real-world deep learning project workflows
Best forLearners who have completed the ML foundations and want to move into deep learning.
Expected outcomeDesign, train, and evaluate deep neural networks for both image and sequence problems.
04
Stage 04

Natural Language Processing

This course applies deep learning to text, one of the most in-demand areas in modern AI. We follow the full pipeline from raw text to trained model, moving from rule-based methods through word embeddings to LSTM classifiers, with projects in sentiment analysis, spam detection, and resume parsing.

Topics you will master
  • Text cleaning, tokenization, and preprocessing pipelines
  • Regular expressions for pattern extraction
  • NLTK and SpaCy for linguistic analysis
  • Sentiment analysis and emotion detection
  • Spam classification systems
  • Word embeddings: word2vec, GloVe
  • LSTM-based text classifiers
  • PDF text extraction and CV/resume parsing
Best forLearners who have completed the deep learning stage and want to specialize in text-based AI applications.
Expected outcomeBuild advanced chatbots, search systems, content moderation tools, or document processing pipelines.
05
Stage 05Final stage

Production Deployment

Most data science courses end at model training. This one begins there. We build REST APIs with FastAPI, containerize with Docker, deploy to AWS EC2 and S3, and serve HuggingFace models such as BERT and ViT in production.

Topics you will master
  • Building REST APIs using FastAPI
  • Deploying ML and NLP models as web services
  • Docker containerization for ML applications
  • AWS EC2 and S3 deployment workflows
  • HuggingFace Transformers integration: BERT, TinyBERT, ViT
  • Streamlit applications for interactive ML demos
  • Production monitoring and operational best practices
Best forLearners who have completed the previous stages and want to become job-ready ML engineers.
Expected outcomeTake any trained model and deploy it as a scalable, production-grade service.

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